Module 3: Chapter 5 Assignment: Representing Language with Vectors

In this video, I walk through my Chapter 5 Natural Language Processing (NLP) assignment, demonstrating the fundamentals of Neural Networks for NLP using Python. The notebook covers how neural networks process text, from simple Bag-of-Words inputs to word embeddings and neural network-based text classification. The assignment begins by introducing the logistic (sigmoid) activation function, explaining how neurons combine weighted inputs and bias to produce predictions. It then demonstrates Bag-of-Words feature extraction, neural network forward propagation, and probability prediction using the sigmoid function. The notebook also explores word embeddings, showing how words can be represented as dense vectors that capture semantic relationships. Finally, it introduces PyTorch, illustrating how neural networks can be implemented and trained for text classification tasks using real-world datasets. Topics Covered Neural Networks for Natural Language Processing Logistic (Sigmoid) Activation Function Artificial Neurons Weighted Sum and Bias Bag-of-Words Representation Forward Propagation Binary Classification Word Embeddings PyTorch Fundamentals Neural Network Training Text Classification Dataset Preparation Machine Learning for NLP Tools & Libraries Python NumPy Pandas Matplotlib scikit-learn PyTorch NLP in Action (2nd Edition) This assignment demonstrates the complete workflow from raw text → numerical representation → neural network prediction, providing a practical introduction to deep learning concepts used in modern NLP applications. If you found this walkthrough helpful, please Like, Share, and Subscribe for more videos on NLP, Machine Learning, Deep Learning, Python, and AI. #NLP #NeuralNetworks #DeepLearning #PyTorch #Python #MachineLearning #ArtificialIntelligence #TextClassification #WordEmbeddings #NaturalLanguageProcessing #DataScience #AI #StudentProject #Coding #Programming #NLPAssignment